一种全局的无关线性图嵌入故障特征提取算法

针对故障特征数据维数高、非线性且系统难以建立物理模型的故障诊断问题,提出了一种全局的无关线性图嵌入故障特征提取算法.通过监督学习建立原始特征的关系图,以线性图嵌入为框架进行特征降维.特征的降维过程既保留了同类数据的局部结构,又考虑了异类数据之间的全局分布,同时最大程度地消除了特征之间的统计相关性.在标准故障数据集上的实验结果表明:与已有的经典算法相比,能更有效地提取出故障的典型特征,因而更有利于故障诊断系统训练网络的快速收敛,实现快速、准确的故障诊断.

Globality-based uncorrelated linear extension of graph embedding for fault feature extraction

Systematic approach to extract the most effective information from original features is of great importance and efficiency for fault detection where physical modeling is highly difficult and the original features are highly dimensional and nonlinear. An algorithm named globality-based uncorrelated linear extension of graph embedding for fault feature extraction was therefore proposed. Supervised learning was used to establish the relationship between original features, and the linear extension of graph embedding was adopted as the feature extraction framework. Great efforts were taken to combine the locality-preserving properties inside the classes and global distribution between different classes, in order to discover both the local and global structure of original features. Information redundancy was greatly reduced by eliminating the statistic correlation between extracted features. Experimental results on standard dataset demonstrate the superiority of this proposed algorithm to many classical feature extraction methods. Thus, a better efficiency in the convergence of training network and in the fault detection can be achieved.